5 day course 
Supporting material 
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Data science and machine learning are two of the most important disciplines to emerge in technology in recent years, and at their heart is the understanding and management of data.
This course will guide you through the world of data science (DS) and machine learning (ML) using the Python programming language and data management principles. Through a combination of lectures, demonstrations and exercises, you’ll gain practical experience applying DS and ML techniques to real data, so it’s perfect for individuals who want to leverage the power of data science and machine learning to drive organisation development.
By the end of the five days, you’ll have a thorough understanding of analytics and automation, and will have developed practical skills in Python targeted towards data analysis so you can create sophisticated models.
Our Data Science and Machine Learning with Python course is available as a private training session that can be delivered via Virtual Classroom or at a location of your choice in South Africa.
Course overview
Who should attend:
This course is perfect for you if you’re a Python developer, or if you’re a coder who is looking to upskill and focus on big data.
What you'll learn:
By the end of this course, you will be able to:
- Set up a powerful environment for data science and machine learning tasks
- Load, visualise and clean big data
- Output and describe descriptive statistics
- Create a range of graphs and data visualisations
- Gain actionable insights from big data
- Confidently assess the structure and quality of data
- Carry out thorough exploratory data analyses
- Define and apply a range of machine learning algorithms
- Understand the end-to-end process of machine learning, from data to prediction
- Use correct machine learning algorithms based on data criteria
- Optimise machine learning models and assess efficacy
- Describe the process, underlying maths, architecture and applications of neural networks
- Create, optimise and apply your own deep learning models for image recognition
Prerequisites
To get the most of out of this course, you should have:
- A working knowledge of Python. You should be confident with basic variable types, functions, if/else, for loops and importing modules
- Basic maths skills: understanding of mean, standard deviation, x/y graphs, logistic and linear regression are a plus
Course agenda
- Basics of data science
- Getting set up for data science
- Introduction to jupyter notebook and gaggle
- Numpy and pandas for data science
- Loading and exploration of data
- Visualisation techniques for data science
- Practical: creating your first data science report
- Variable types
- Correcting data
- Completing data
- Creating data (feature engineering)
- Converting data
- Practical: real-life data set
- Introduction to ML
- Foundations: linear and logistic regression
- Cost, loss, bias and variance
- Using scikit learn
- Practical: linear regression in practice
- Commonly used ML models
- Real-world use cases
- Practical: choosing and applying an ML model
- Assessing results
- Improving model output
- Practical: your end-to-end ML project
- A conceptual introduction to deep learning and neural networks
- Underlying maths
- Neural network architecture
- Practical: applying NNs
- Optimising NNs
- Image classification using neural networks
- Taking these skills forward